Fault identification and localization for the fast switching valve in the equal-coded digital hydraulic system based on hybrid CNN-LSTM model

Digital hydraulic systems are composed of parallel fast switching valves (FSVs) and have unique fault tolerance characteristics, while fault identification and localization are the premise of fault tolerance. However, due to the similar fault features, it is difficult to accurately diagnose the faulty valve in an equal-coded digital hydraulic system (EDHS) without its additional sensors. Aiming at solving the problem by relying on the self-contained sensor information only, the identification and localization method for the faulty valve in EDHS based on system pressure information is proposed in this paper, which is the combination of convolutional neural network (CNN) and long short-term memory network (LSTM) with the multi-attribute time series data. Firstly, the flowrate mathematical models of the FSV, the digital flow control unit (DFCU) and its EDHS system are established under typical faults. On this basis, the system performance for typical faults in DFCU on the pump and tank side under different operating conditions are simulated and analyzed. Furthermore, the system pressures under normal and fault conditions, input control signals and the system pressures are used as the database, that is the multi-attribute time series data. And then, the time-space network fault diagnosis model with the combination of CNN and LSTM is developed, and the model reliability and independence are verified by indexes of precision, recall rate, and F1. The results show that the average precision of the hybrid CNNLSTM model is up to 98.68%, achieving efficient diagnosis performance for faulty valves, which could contribute to fault tolerance of digital hydraulic systems.